[Paleopsych] Intelligent Bacteria

[Steve Hovland]

Our own intelligence is mediated by neuropeptides
and enzymes, so if we find those in bacteria,
we find intelligence...

Steve Hovland
www.stevehovland.net


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Subject: [Paleopsych] Intelligent Bacteria

 

http://www.world-science.net/exclusives/050418_bactfrm.htm

 

Intelligent bacteria?

For the most primitive beings in the web of life, some researchers
claim, "simple" might not mean "stupid." 

Posted April 18, 2005
Special to World Science

Bacteria are by far the simplest things alive, at least among things
generally agreed on as being alive. Next to one of these single-celled
beings, one cell of our bodies looks about as complex as a human does
compared to a sponge. 

 <http://www.hometown.aol.com/scipage/images/plate-CT-docu0043b.jpg> 

A colony of Paenibacillus dendritiformis bacteria, which some
researchers say can organize themselves into different types of
extravagant formations to maximize food intake for given conditions.
According to some, this reflects a bacterial intelligence. (Courtesy
Eshel Ben-Jacob <http://star.tau.ac.il/~inon/pictures/pictures.html> ,
Tel Aviv University, Israel)

  _____  

Yet the humble microbes may have a rudimentary form of intelligence,
some researchers have found.

The claims seem to come as a final exclamation point to a long series of
increasingly surprising findings of sophistication among the microbes,
including apparent cases of cooperation and even altruism. 

But there is no clear measurement or test that scientists can use, based
on the behavior alone, to determine whether it reflects intelligence.

Some researchers, though, have found a systematic way of addressing the
question and begun looking into it. This method involves focusing not so
much on the behavior itself as the nuts and bolts behind it-a complex
system of chemical "signals" that flit both within and among bacteria,
helping them decide what to do and where to go.

Researchers have found that this process has similarities to a type of
human-made machine designed to act as a sort of simplified brain. These
devices solve some simple problems in a manner more human-like than
machine-like.

The devices, called neural networks, also run on networks of signals
akin to those of the bacteria. The devices use the networks to "learn"
tasks such as distinguishing a male from a female in photographs-typical
sorts of problems that are easy for humans but hard for traditional
computers.

The similarities in the bacterial and neural network signaling systems
are far more than superficial, wrote one researcher, Klaas J.
Hellingwerf, in the April issue of the journal Trends in Microbiology.
He found that the bacterial system contains all the important features
that make neural networks work, leading to the idea that the bacteria
have "a minimal form of intelligence."

Bacterial signaling possesses all four of the key properties that neural
network experts have identified as essential to make such devices work,
Hellingwerf elaborated. The only weak link in the argument, he added, is
that for one of those properties, it's not clear whether bacteria
exhibit it to a significant extent. This may be where future research
should focus, he wrote.

Cooperation and altruism

The comparison of bacterial signaling with neural networks is not the
only evidence that has nudged researchers closer to the concept that
bacteria might possess a crude intelligence-though few scientists would
go as far as to use that word. 

One of the other lines of evidence is a simple examination of bacterial
behavior.

This behavior is strikingly versatile, researchers have found in recent
years; bacteria can cope with a remarkably wide range of situations by
taking appropriate actions for each. For instance, the deadly
Pseudomonas aeruginosa can make a living by infecting a wide variety of
animal and plant tissues, each of which is a very different type of
environment in which to live and find sustenance.

Furthermore, bacteria cooperate: they can group together to take on
tasks that would be difficult or impossible for one to handle alone. In
a textbook example, millions of individuals of the species Myxococcus
xanthus can bunch up to form a "predatory" colony that moves and changes
direction collectively toward possible food sources.

Some examples of bacterial cooperation have even led researchers to
propose that bacteria exhibit a form of altruism. For instance, some
strains of Escherichia coli commit suicide when infected by a virus,
thereby protecting their bacterial neighbors from infection.

But until recently, few or no scientists had seriously suggested these
behaviors reflected intelligence.  

For instance, bacterial "altruism" may be a simple outcome of evolution
that has nothing to do with concern for the welfare of others, wrote the
University of Bonn's Jan-Ulrich Kreft in last August's issue of the
research journal Current Biology. Thus he didn't suggest that any
process akin to thinking was at work.

But one thing that ties these various behaviors together is that they
all operate as a result of signaling mechanisms like the ones studied by
Hellingwerf.

Mousetraps, learning and language

These mechanisms work in a way somewhat akin to the American board game
Mousetrap. In this game, you try to catch your opponent's plastic mouse
using a rambling contraption that starts working when you turn a crank.
This rotates gears, that push a lever, that moves a shoe, that kicks a
bucket, that sends a ball down stairs and-after several more
hair-raising steps of the sort-drops a basket on the mouse.

Molecular signals inside cells work through somewhat similar chain
reactions, except the pieces involved are molecules. 

A typical way these molecular chain reactions work is that small
clusters of atoms, called phosphate groups, are passed among various
molecules. One example of what such a system could accomplish: a bit of
food brushing against the cell could start a series of events that lead
inside the cell and activate genes that generate the chemicals that
digest the food.

A single bacterium can contain dozens of such systems operating
simultaneously for different purposes. And compared to the board game,
the cellular systems have additional features that make them more
complicated and versatile.

For instance, some of these bacterial contraptions, when set in motion,
lead to the formation of extra copies of themselves. These tricks can
lead to phenomena with aspects of learning and language.

For example, a shortage of a nutrient in a bacterium's neighborhood can
activate a system that makes the microbe attract the nutrient toward
itself more strongly. The system also produces extra copies of itself,
researchers have found. Thus if shortage recurs later, the bacterium is
better prepared. This is a form of "learning," Hellingwerf and
colleagues wrote in the August, 2001 issue of the Journal of
Bacteriology. 

Brain cells can operate in an analogous way: a brain cell can grow more
sensitive to a signal that it receives repeatedly, resulting in a
reinforcement of signaling circuits and learning. 

The bacterial versions of "mousetrap" have other tricks as well. For
instance, some of them seem to contain components influenced by not just
one stimulus, but by two or more. Thus the chain reactions merge. The
component receiving these stimuli adds the strength of each to give a
response whose strength is proportional to the sum.

Although the full complexities of bacterial signaling are far from
understood, many researchers believe the systems helps bacteria to
communicate.

For instance, some bacteria, when starving, emit molecules that serve as
stress signals to their neighbors, write Eshel Ben-Jacob of Tel Aviv
University and colleagues in last August's issue of Trends in
Microbiology. The signals launch a process in which the group can
transform itself to create tough, walled structures that wait out tough
times to reemerge later.

This transformation involves a complex dialogue that reveals a "social
intelligence," the researchers added. Each bacterium uses the signals to
assess the group's condition, compares this with its own state, and
sends out a molecular "vote" for or against transformation. The majority
wins.

Collectively, the researchers wrote, "bacteria can glean information
from the environment and from other organisms, interpret the information
in a 'meaningful' way, develop common knowledge and learn from past
experience." Some can even collectively change their chemical "dialect"
to freeze out "cheaters" who exploit group efforts for their own selfish
interest, the researchers claimed.

Not everyone is convinced by these claims. 

Rosemary J. Redfield of the University of British Columbia, Vancouver,
has argued that the supposed communication molecules actually exist
mainly to tell bacteria how closed-in their surroundings are, which is
useful information to them for various reasons.

Inside-out

To properly assess if bacterial signals constitute intelligence, whether
of a social or individual brand, Hellingwerf and some other researchers
work from the inside out. 

Rather than focusing on the behaviors, which are open to differing
interpretations, they focus on the systems of interactions followed by
the molecules. These systems, it is hoped, have distinct properties that
can be measured and compared against similar interactions in known
intelligent beings.

For instance, if these bacterial systems operate similarly to networks
in the brain, it would provide a weighty piece of evidence in favor of
the bacterial intelligence.

Hellingwerf has set himself a more modest goal, comparing bacterial
signaling not to the brain, but to the brain-like, human-made neural
network devices. Such an effort has a simple motivation. Demonstrating
that bacterial signaling possesses every important feature of neural
networks would suggest at least that microbial capabilities rival those
of devices with proven ability to tackle simple problems using known
rules of brain function-rather than robot-like calculations, which are
very different.

To understand how one could do such a comparison requires a brief
explanation of how neural networks work, and how they differ from
traditional computers. 

Computers are good at following precise instructions, but terrible at
even simple, common-sense tasks that lack definite rules, like the
recognition of the difference between male and female.

Neural networks, like humans, can do this because they are more
flexible, and they learn-even though they can be built using computers.
They are a set of simulated "brain cells" set to pass "signals" among
themselves through simulated "connections."

Some information that can be represented as a set of numbers, such as a
digitized photograph, is fed to a first set of "cells" in such a way
that each cell gets a number. Each cell is then set to "transmit" all,
part or none of that number to one or more other cells. How big a
portion of the number is passed on to each, depends on the simulated
"strength" of the connections that are programmed into the system.

Each of those cells, in turn, are set to do something with the numbers
they receive, such as add them or average them-and then transmit all or
part of them to yet another cell.

Numbers ricochet through the system this way until they arrive at a
final set of "output" cells. These cells are set to give out a final
answer-based on the numbers in them-in the form of yet another number.
For example, the answer could be 0 for male, 1 for female.

Such a system, when new, will give random answers, because the
connections are initially set at random. However, after each attempt at
the problem, a human "tells" the system whether it was right or wrong.
The system is designed to then change the strength of the connections to
improve the answer for the next try. 

To do this, the system calculates to what extent a change in strength of
each connection previously contributed to giving a right or wrong
answer. This information tells the system how to change the strengths to
give better results. Over many attempts, the system's accuracy gradually
improves, often reaching nearly human-like performance on a given task. 

Such systems not only work quite well for simple problems, many
researchers believed they capture all the key features of real brain
cells, though in a drastically simplified way.

The devices also have similarities to the messaging systems in bacteria.
But how deep are the resemblances? To answer this, Hellingwerf looked at
four properties that neural-network experts have identified as essential
for such devices to work. He then examined whether bacterial signaling
fits each of the criteria.

The four properties are as follows. 

First, a neural network must have multiple sub-systems that work
simultaneously, or "in parallel." Neural networks do this, because
signals follow multiple pathways at once, in effect carrying out
multiple calculations at once. Traditional computers can't do this; they
conduct one at a time. Bacteria do fit the standard, though, because
they can contain many messaging networks acting simultaneously,
Hellingwerf observes.

Second, key components of the network must carry out logical operations.
This means, in the case of a neural network, that single elements of the
network combine signals from two or more other elements, and pass the
result on to a third according to some mathematical rule. Regular
computers also have this feature. Bacteria probably do too, Hellingwerf
argues, based on the way that parts of their signaling systems add up
inputs from different sources.

The third property is "auto-amplification." This describes the way some
network elements can boost the strength of their own interactions.
Hellingwerf maintains that bacteria show this property, as when, for
example, some of their signaling systems create more copies of
themselves as they run.

The fourth property is where the rub lies for bacteria. This feature,
called crosstalk, means that the system must not consist just of
separate chain reactions: rather, different chain reactions have to
connect, so that the way one operates can change the way another runs.

Crosstalk is believed to underlie an important form of memory called
associative memory, the ability to mentally connect two things with no
obvious relationship. A famous example is the Russian scientist Ivan
Pavlov's dog, who drooled at the ring of a bell because experience had
taught him food invariably followed the sound.

Crosstalk has been found many times in bacteria, Hellingwerf wrote-but
the strength of the crosstalk "signals" are hundreds or thousands of
times weaker than those that follow the main tracks of the chain
reactions. Moreover, "clear demonstrations of associative memory have
not yet been detected in any single bacterial cell," he added, and this
is an area ripe for further research. If bacteria can indeed
communicate, it seems they may be holding quite a bit back from us. 

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